The Analysis, Synthesis, and Description of Biological Images

Lipkin, Lewis E.; Watt, W. C.; Kirsch, Russell A.

doi:10.1111/j.1749-6632.1965.tb11712.x

Cited by 42 publications

(1 citation statement)

References 10 publications

(1 reference statement)

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“…Microscopy images potentially contain a wealth of information about the morphological, structural, and dynamical characteristics of tissues, cells, and molecules, which may go unnoticed even to the expert human eye [4] , [5] , [6] . However, designing computer algorithms to extract this information with high fidelity is a great challenge, as has been well recognized since the mid-1960s, after the first decade of serious attempts [7] , [8] , [9] , and is still true today.…”

Section: Introductionmentioning

confidence: 99%

A bird’s-eye view of deep learning in bioimage analysis

Meijering

2020

Computational and Structural Biotechnology Journal

113

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Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering. Also in biology and medicine, deep learning technologies are fundamentally transforming how we acquire, process, analyze, and interpret data, with potentially far-reaching consequences for healthcare. In this mini-review, we take a bird’s-eye view at the past, present, and future developments of deep learning, starting from science at large, to biomedical imaging, and bioimage analysis in particular.

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Section: Introductionmentioning

confidence: 99%

A bird’s-eye view of deep learning in bioimage analysis

Meijering

2020

Computational and Structural Biotechnology Journal

113

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Toward computerized morphometric facilities: A review of 58 software packages for computer‐aided three‐dimensional reconstruction, quantification, and picture generation from parallel serial sections

Huijsmans¹,

Lamers²,

Los³

et al. 1986

Anat. Rec.

101

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This review gives an inventory of 58 computer-aided three-dimensional reconstruction applications in the domain of biomedical research. It is devoted to the formulation of a set of recommendations thought to be necessary for improved performance of software packages in this field. These recommendations can be used to select packages and to guide future developments of existing reconstruction systems. The survey is restricted to three-dimensional reconstructions based upon a series of parallel sections of an object. Subjects treated are programming languages, resolution and sampling, input preparation, realignment, local deformation of slices, numerical quantifications, topological complexity, internal representation, display complexity (hidden surfaces, shading, smoothing), structure extraction, descriptive elements, database, data compression, time efficiency of systems and algorithms, hardware configuration, input devices, input media, interactive aids, display devices, and output devices. Information for this survey comes from articles that appeared between 1965 and 1985.

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NIST physical standards for DNA‐based medical testing

Barker¹,

Watson²,

Ticehurst³

et al. 2002

Clinical Laboratory Analysis

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As DNA and RNA become major targets for clinical laboratory analysis, benchmark reagents will play an increasingly important role in standardization. Reliable national and international nucleic acid standards promote automation and third-party reimbursement for clinical testing. Furthermore, nucleic acid standards provide materials for quality assurance and quality control (QA/QC), and proficiency testing. Standard methods and training initially evolved from consensus guidelines endorsed by professional societies and governmental agencies. The National Institute of Standards and Technology (NIST), a nonregulatory agency of the U.S. Department of Commerce, develops and certifies physical and chemical standards in support of national commerce, manufacturing, and science. In its role supporting U.S. science and industry, the NIST responds to specific standards needs, most recently for medically and biologically important analytes. Broad-based consensus developed through interdisciplinary NIST workshops initiated development of NIST-certified DNA standards. Such materials serve the diagnostic community and help manufacturers benchmark a variety of DNA diagnostic testing platforms. Here we summarize the NIST experience and programs for development of national standards for DNA-based medical diagnostic testing.

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The Analysis, Synthesis, and Description of Biological Images

Cited by 42 publications

References 10 publications

A bird’s-eye view of deep learning in bioimage analysis

A bird’s-eye view of deep learning in bioimage analysis

Toward computerized morphometric facilities: A review of 58 software packages for computer‐aided three‐dimensional reconstruction, quantification, and picture generation from parallel serial sections

NIST physical standards for DNA‐based medical testing

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